At the end of the 1950s, the possibility of machine learning in computers began to be discussed. After more than half a century, thanks to technological advances, machine learning is more than a reality; it is a tool that supports different fields, such as education, business, science, and management. This chapter explores how machine learning takes place in decision-making, especially in the managerial field, and the advantages that it offers when studying scenarios, choosing strategies, exploring the possible consequences of our actions, and/or predicting the likely responses of the parties involved in a specific situation.
TopDecision-Making
Making decisions is a constant and inherent activity in life. Everything we do —except for some biological functions— and how we do it has gone through a decision-making process. The aforementioned process can occur in fractions of seconds or be the result of long periods studying variables and probable scenarios, according to the possible decisions that are made.
Three elements are used to explain decision-making: process conception, choice of course of action, and the solution of problems or situations of organizational opportunity (Arévalo & Estrada 2017, p. 253).
However, in general, it can be said that the decision-making process is made up of a decision-maker —could be more than one—, the variables to be considered, the possible actions, and the study of their possible consequences.
Depending on the approach, the decision-maker can be rational, satisfying, organizational procedure approach, and political.
Simon (1980), defines the figure of the rational decision maker. In this case, the possible alternatives are identified and listed, the consequences derived from each one is analysed and these consequences are assessed and compared. As for the decision maker, he/she must describe his/her utility function, that is, his/her preference for different consequences (Canós et al, n.d., p.2).